Federated Graph AGI for Cross-Border Insider Threat Intelligence in Government Financial Schemes

TL;DR

This paper introduces FedGraph-AGI, a federated learning framework that combines graph neural networks and AGI reasoning to detect insider threats across borders while preserving privacy, achieving high accuracy and scalability.

Contribution

It presents the first integration of AGI reasoning with federated graph learning for cross-border insider threat detection in financial schemes.

Findings

Achieves 92.3% accuracy on a 50,000-transaction dataset across 10 jurisdictions.

Outperforms federated baselines (86.1%) and centralized methods (84.7%).

AGI reasoning adds a 6.8% accuracy improvement, and MoE adds 4.4%.

Abstract

Cross-border insider threats pose a critical challenge to government financial schemes, particularly when dealing with distributed, privacy-sensitive data across multiple jurisdictions. Existing approaches face fundamental limitations: they cannot effectively share intelligence across borders due to privacy constraints, lack reasoning capabilities to understand complex multi-step attack patterns, and fail to capture intricate graph-structured relationships in financial networks. We introduce FedGraph-AGI, a novel federated learning framework integrating Artificial General Intelligence (AGI) reasoning with graph neural networks for privacy-preserving cross-border insider threat detection. Our approach combines: (1) federated graph neural networks preserving data sovereignty; (2) Mixture-of-Experts (MoE) aggregation for heterogeneous jurisdictions; and (3) AGI-powered reasoning via Large Action Models (LAM) performing causal inference over graph data. Through experiments on a 50,000-transaction dataset across 10 jurisdictions, FedGraph-AGI achieves 92.3% accuracy, significantly outperforming federated baselines (86.1%) and centralized approaches (84.7%). Our ablation studies reveal AGI reasoning contributes 6.8% improvement, while MoE adds 4.4%. The system maintains epsilon = 1.0 differential privacy while achieving near-optimal performance and scales efficiently to 50+ clients. This represents the first integration of AGI reasoning with federated graph learning for insider threat detection, opening new directions for privacy-preserving cross-border intelligence sharing.